title: Quasi-cellular systems: stochastic simulation analysis at nanoscale range.
abstract: BACKGROUND
  The wet-lab synthesis of the simplest forms of life (minimal cells) is a challenging aspect in modern synthetic biology. Quasi-cellular systems able to produce proteins directly from DNA can be obtained by encapsulating the cell-free transcription/translation system PURESYSTEM(PS) in liposomes. It is possible to detect the intra-vesicle protein production using DNA encoding for GFP and monitoring the fluorescence emission over time. The entrapment of solutes in small-volume liposomes is a fundamental open problem. Stochastic simulation is a valuable tool in the study of biochemical reaction at nanoscale range. QDC (Quick Direct-Method Controlled), a stochastic simulation software based on the well-known Gillespie's SSA algorithm, was used. A suitable model formally describing the PS reactions network was developed, to predict, from inner species concentrations (very difficult to measure in small-volumes), the resulting fluorescence signal (experimentally observable).
  
  
  RESULTS
  Thanks to suitable features specific of QDC, we successfully formalized the dynamical coupling between the transcription and translation processes that occurs in the real PS, thus bypassing the concurrent-only environment of Gillespie's algorithm. Simulations were firstly performed for large liposomes (2.67µm of diameter) entrapping the PS to synthetize GFP. By varying the initial concentrations of the three main classes of molecules involved in the PS (DNA, enzymes, consumables), we were able to stochastically simulate the time-course of GFP-production. The sigmoid fit of the GFP-production curves allowed us to extract three quantitative parameters which are significantly dependent on the various initial states. Then we extended this study for small-volume liposomes (575 nm of diameter), where it is more complex to infer the intra-vesicle composition, due to the expected anomalous entrapment phenomena. We identified almost two extreme states that are forecasted to give rise to significantly different experimental observables.
  
  
  CONCLUSIONS
  The present work is the first one describing in the detail the stochastic behavior of the PS. Thanks to our results, an experimental approach is now possible, aimed at recording the GFP production kinetics in very small micro-emulsion droplets or liposomes, and inferring, by using the simulation as a reverse-engineering procedure, the internal solutes distribution, and shed light on the still unknown forces driving the entrapment phenomenon.
authors.affiliation: Dipartimento di Informatica, Università di Pisa, L.go B. Pontecorvo 3, 56127 Pisa, Italy.
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Calviello
authors.forename: Pasquale
authors.initials: P
authors.lastname: Stano
authors.forename: Fabio
authors.initials: F
authors.lastname: Mavelli
authors.forename: Pier Luigi
authors.initials: PL
authors.lastname: Luisi
authors.forename: Roberto
authors.initials: R
authors.lastname: Marangoni
mesh_terms: D000465:Algorithms; D002474:Cell-Free System; D003198:Computer Simulation; D049452:Green Fluorescent Proteins / Q000032:analysis / Q000378:metabolism; D007700:Kinetics; D008081:Liposomes / Q000378:metabolism; D014176:Protein Biosynthesis; D012984:Software; D013269:Stochastic Processes; D058615:Synthetic Biology / Q000379:methods*; D014158:Transcription, Genetic
chemical_list: D008081:Liposomes; D049452:Green Fluorescent Proteins